Patrimony

The Louis Bachelier Group's patrimony has been defined as all the publications produced by academic researchers thanks to Group funding (ILB, FdR, IEF, Labex) or via the use of EquipEx data (BEDOFIH, EUROFIDAI).


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Machine learning algorithms in insurance: solvency, textmining, anonymization and transparency.

Actuarial Science, Analyse Sémantique, Anonimysation, Anonimyzation, Apprentissage statistique, Machine Learning, Ocr, Science actuarielle, Text Mining, Transparence, Transparency

Micro-macro approaches to age-structured heterogeneous population dynamics. Application to self-exciting processes and demography.

Actuarial science, Age pyramid, Birth-death processes, Causes-of-death mortality, Censure par intervalle, Dynamique des populations, Démographie mathématique, Fast life trajectories, Hawkes processes, Heterogeneity, Hétérogénéité, Interval censoring, Limit theorems for stochastic processes, Longevity, Longévité, Mathematical demography, Maximum de vraisemblance paramétrique, Microsimulation, Modèles multi-états, Mortality, Mortalité, Mortalité par causes de décès, Multi-state models, Parametric maximum likelihood, Point processes, Population dynamics, Processus de Hawkes, Processus de naissance et mort, Processus ponctuels, Pyramide des âges, Science actuarielle, Théorèmes limites pour les processus, Trajectoires de vie accélérées

Micro-macro analysis of heterogenous age-structured populations dynamics. Application to self-exciting processes and demography.

Actuarial science, Dynamique des populations, Démographie mathématique, Longévité, Mathematical demography, Microsimulation, Population dynamics, Processus de naissance et mort, Science actuarielle

Micro-macro approaches to age-structured heterogeneous population dynamics. Application to self-exciting processes and demography.

Actuarial science, Dynamique des populations, Démographie mathématique, Longévité, Mathematical demography, Microsimulation, Population dynamics, Processus de naissance et mort, Science actuarielle

Machine learning algorithms in insurance: solvency, textmining, anonymization and transparency.

Actuarial Science, Analyse Sémantique, Anonimysation, Anonimyzation, Apprentissage statistique, Machine Learning, Ocr, Science actuarielle, Text Mining, Transparence, Transparency

Sequential Monte Carlo samplers to fit and compare insurance loss models.

Actuarial Science, Bayesian statistics, Composite model, Sequential Monte Carlo sampler